With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Existing solutions are mainly driven by small …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …

We consider the problem of zero-shot recognition: learning a visual classifier for a category
with zero training examples, just using the word embedding of the category and its …

To understand a scene in depth not only involves locating/recognizing individual objects, but
also requires to infer the relationships and interactions among them. However, since the …

This paper provides a review and commentary on the past, present, and future of numerical
optimization algorithms in the context of machine learning applications. Through case …

Image-text matching plays a central role in bridging vision and language. Most existing
approaches only rely on the image-text instance pair to learn their representations, thereby …